{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration:      5,   Func. Count:     37,   Neg. LLF: 31218.536838353157\n",
      "Iteration:     10,   Func. Count:     68,   Neg. LLF: 30776.788466864997\n",
      "Optimization terminated successfully    (Exit mode 0)\n",
      "            Current function value: 30776.192498030065\n",
      "            Iterations: 14\n",
      "            Function evaluations: 87\n",
      "            Gradient evaluations: 14\n",
      "                     Constant Mean - GARCH Model Results                      \n",
      "==============================================================================\n",
      "Dep. Variable:                   None   R-squared:                       0.000\n",
      "Mean Model:             Constant Mean   Adj. R-squared:                  0.000\n",
      "Vol Model:                      GARCH   Log-Likelihood:               -30776.2\n",
      "Distribution:                  Normal   AIC:                           61560.4\n",
      "Method:            Maximum Likelihood   BIC:                           61592.9\n",
      "                                        No. Observations:                25105\n",
      "Date:                Mon, Jan 17 2022   Df Residuals:                    25104\n",
      "Time:                        00:56:36   Df Model:                            1\n",
      "                                 Mean Model                                 \n",
      "============================================================================\n",
      "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
      "----------------------------------------------------------------------------\n",
      "mu             0.0684  5.679e-03     12.043  2.109e-33 [5.726e-02,7.952e-02]\n",
      "                              Volatility Model                              \n",
      "============================================================================\n",
      "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
      "----------------------------------------------------------------------------\n",
      "omega          0.0136  2.383e-03      5.724  1.041e-08 [8.971e-03,1.831e-02]\n",
      "alpha[1]       0.1061  8.851e-03     11.985  4.254e-33   [8.873e-02,  0.123]\n",
      "beta[1]        0.8826  8.835e-03     99.909      0.000     [  0.865,  0.900]\n",
      "============================================================================\n",
      "\n",
      "Covariance estimator: robust\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from arch import arch_model\n",
    "\n",
    "data = pd.read_csv(\"dataset/factors.csv\")\n",
    "# print(data.head())\n",
    "\n",
    "mkt_returns = data['Mkt-RF'] +  data['RF']\n",
    "returns = mkt_returns.dropna()\n",
    "# print(returns.tail())\n",
    "\n",
    "am = arch_model(returns)\n",
    "res = am.fit(update_freq=5)\n",
    "print(res.summary())\n",
    "\n",
    "fig = res.plot(annualize='D')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
  },
  "kernelspec": {
   "display_name": "Python 3.7.3 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
